Look Before You Leap: Pre-Action Verification for LLM Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
An LLM agent acts on the world by emitting actions: shell commands to run, edits to apply. A wrong action does not always fail loudly; it can fail silently, producing a plausible but incorrect effect that raises no error. We argue that a cheap deterministic check, run before an action takes effect, is an effective and underused form of agent oversight, and we study it across two action modalities in one framework. The idea is to fix an action's correct effect by construction, before any executor runs, so that silent failure is measured directly and the verifier may abstain rather than guess. For shell commands, a static verifier over 9930 commands and 482 tools catches 95.8% of invalid commands at a 10.0% false-positive rate. Its syntax and binary checks are oracle-exact, giving zero false positives while catching half of all errors; the flag check is bounded only by help-text coverage and accounts for every false positive. For code edits, a benchmark of 640 edits over 224 files isolating the apply step exposes a sharp split. Content-anchored formats such as search/replace and diff fail cleanly, whereas location-anchored formats fail silently: line numbers corrupt 99.1% of files under a one-line shift, and function-name edits hit the wrong function 12.7% of the time. In both settings a refuse-when-unsure policy turns silent failures into recoverable ones at a tunable cost in applicability: selective grounding reaches 0.958 recall at 7.0% false positives, and an anchor-and-verify applier records one silent misapplication in 8320 trials (0.01%). We release both benchmarks, the verifiers, and the guards.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 9 of its 16 references that are in the map. First posted on arXiv 9 Aug 2026.
Builds on
9 of 16 references.
References · 9
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
- 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
- 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 421
- 2018AI safety via debatecited by 400
- 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 238
- 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 197
- 2018NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating Systemcited by 98
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
- 2026CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents
- 2025SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents
- 2026When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
- 2026When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems
- 2026SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents
- 2026TomasuLLM: Out-of-Order Speculative Execution for LLM Agents
- 2026Silence Is Endorsement: Verification-Status Laundering in LLM Agent Pipelines